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Experimental investigations and field applications showed an increase in oil recovery by using low salinity water injection. The mechanisms leading to the increase in oil recovery by low salinity water injection are debatable. Furthermore, low salinity water injection requires relatively fresh water to dilute sea water which is not available in many areas around the world especially the Arabian Gulf region. Using low salinity water as an enhanced oil recovery process is a costly process because it requires a lot of fresh water to dilute the sea water.In this study we introduce an economic and new chemical enhanced oil recovery fluid capable of recovering additional oil from sandstone reservoirs after the primary water flooding stage. The new chemical enhanced oil recovery fluid utilizes high pH chelating agents at low concentrations prepared in sea water without any dilution. Coreflood experiments were performed using Berea sandstone cores at 100 o C to test the effectiveness of this new EOR fluid system.The results of the coreflooding experiments showed that the new fluid system is able to recover additional oil up to 30% from the initial oil in place after sea water flooding. Zeta potential measurements were performed using the new fluid system and crushed Berea cores to identify the mechanism leading to this additional oil recovery. The results of the zeta potential revealed more negative values for the new system more than the sea water and low salinity water, confirming that the rock wettability is altered to a more water-wet condition. This changed in rock wettability could be due to rock dissolution and the chelation of the different ions such as calcium, magnesium and iron ions. The new fluid system very attractive as its cost is low compared to the cost of diluting the sea water in the case of low salinity water flooding.

PurposeThe purpose of this paper is to analyze the reliability of the quantitative risk model used for planning inspection and maintenance activities. The objective is to critically discuss the factors that contribute to the probability and consequence of failure calculations.Design/methodology/approachThe case study conducted using one of the most widely deployed risk models in the oil and gas industry where a full assessment was performed on an offshore gas producing platform.FindingsThe generic failure frequencies used as the basis for calculating the probability of failure are set at a value representative of the refining and petrochemical industry's failure data. This failure database does not cover offshore. The critical discussion indicated the lack of basis of the coefficient of variances, prior probabilities and conditional probabilities. Moreover, the risk model does not address the distribution of thickness measurements, corrosion rates and inspection effectiveness, whereas only overall deterministic values are used; this requires judgment to determine these values. Probabilities of ignition, probabilities of delayed ignition and other probabilities in Level 1 event tree are found selected based on expert judgment for each of the reference fluids and release types (i.e. continuous or instantaneous). These probabilities are constant and independent of the release rate or mass and lack of constructed model. Defining the release type is critical in the consequence of the failure methodology, whereas the calculated consequences differ greatly depending on the type of release, i.e. continuous or instantaneous. The assessment results show that both criteria of defining the type of release, i.e. continuous or instantaneous, do not affect the calculations of flammable consequences when the auto-ignition likely is zero at the storage temperature. While, the difference in the resulted toxic consequence was more than 31 times between the two criteria of defining the type of release.Research limitations/implicationsThere is a need to revamp this quantitative risk model to minimize the subjectivity in the risk calculation and to address the unique design features of offshore platforms.Originality/valueThis case study critically discuss the risk model being widely applied in the O&G industry and demonstrates to the end-users the subjectivity in the risk results. Hence, be vigilant when establishing the risk tolerance/target for the purpose of inspection and maintenance planning.

Chemical, petrochemical, and refinery sectors have been facing tougher safety, environmental and mechanical integrity regulations as well as challenges associated with the need for cost reduction to improve competitiveness. Risk‐based Inspection (RBI) is a cost‐effective approach to manage operational risks by making an informed decision on inspection frequency, extent of inspections, and types of non‐destructive testing. This paper presents a comprehensive practical risk model that can simulate all components of liquefied petroleum gas (LPG) tanks, that is, roof, shell, nozzles, and bottom plates. The probability of failure (PoF) model was developed based on failure frequency data from four different sources to determine the adequate general failure frequencies, moreover, it addresses the fatigue effect due to cyclic loading. Reviewing past losses of containment incidents revealed that fires and explosion are the most likely outcomes, therefore, this risk model introduced area affected consequence of failure (CoF) simulation. The CoF model addresses all the main components of the LPG tank and deploys a practical gas dispersion model to estimate the vapor cloud explosion (VCE) and flash fire consequence affected areas. Furthermore, modified mass release equations are presented to account for the decline in the release rate with time to determine the pool fire area and leak duration. Consequence areas are determined based on serious personnel injuries and component damage from thermal radiation and explosions. To validate the risk model, a proof of concept assessment was performed on seven tanks.

One of the most important parameters affecting flow rate in oil producing wells is the pressure drop across the surface flow-lines. The pressure drop calculation in multiphase flow is very complicated due to the empirical nature of the correlations used and the high variation in gas and liquid hold up especially in hilly terrain and rough environment that will complicate the flow regime and make negative impact on well productivity. Scientists came up with two main approaches: empirical/experimental flow correlations and mechanistic models to overcome this difficulty. These two approaches are applicable within certain conditions where their accuracy in pressure drop prediction degrades outside their design boundary ranges. The raising popularity of Artificial Intelligence (AI) techniques during the past two decades proved that AI can be an alternative solution to many of the problems where physics and classic statistics fail to provide satisfactory solutions. This paper describes the utilization of Fuzzy Logic and Neural Networks, which is one of the industry AI techniques in predicting the multiphase flow pressure drop in surface pipeline for oil fields using real testing data collected from oil fields. More than 240 published real well testing data were used in constructing the model. After filtering the data and building the model, the newly developed AI model was the best method to predict the multiphase flow pressure drop. Prosper software was used to confirm the validity of the AI methods over the other existing correlations. The final results confirmed that Adaptive Neuro-Fuzzy Inference System (ANFIS) is more accurate than all the used correlations and Neural Networks. The ANFIS model resulted in .4% absolute average error compared to a range of 17.5% – 64.57 % for the compared correlations.

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The evaluating of the pressure drop due to multiphase flow in vertical pipes and inclined pipes is crucial for oil and gas industry. Numerous correlations and models have been developed to calculate the pressure drop in vertical wells but the effectiveness of these models is still under debate. Most of the correlations and models were developed to calculate the pressure drop due to multiphase flow based on accurately and reliably measured flow parameters. However, they can work best within the proposed data ranges. Their accuracy degrades if they are used for data out of the measured ranges.Artificial Intelligence (AI) has proven to be an alternative solution to many of the problems where physics and classic statistics fail to provide satisfactory solutions due to limiting assumptions and complicated reality. Different AI methods, viz., Fuzzy Logic (ANFIS), Neural Networks (ANN), Support Vector Machine (SVM), and Decision tree (DT) were used in predicting the multiphase flow pressure drop in surface pipeline and production tubing for oil fields. 239 published real field datasets were used in constructing the flow line model and about 795 datasets were used to construct the pressure drop from the well head pressure to the bottom hole flowing pressure (tubing model).The models were tested by dividing the data into three categories:, (60%) were used for training and (15%) for validation, and (25%) for testing. The results of testing showed that ANN, ANFIS, and SVM give better predictions than the common correlations in case of pressure drop from the wellhead to the GOSP (flow line) or from the wellhead to the bottom hole location (tubing). The average absolute percentage error (AAPE) was 1.15% for ANN, 0.39% for ANFIS, and 9% for SVM, and 77% for DT for the flow line model and the error was 3.8% for ANN, 4.5% for ANFIS, 4.3% for SVM, and 3% for DT for the tubing model.

Managed Pressure Drilling technology became popular and widespread in Western countries in the early 2000s and has long been successfully used for drilling complex wells onshore and offshore projects (for example in the North Sea, Gulf of Mexico and etc.) In Russia this technology has found its application relatively recently and still has never been used for offshore drilling. This article describes the results of the first MPD offshore application in Russia for drilling an HTHP exploration well in the Caspian Sea. A fully automated MPD set with early kick detection system (EKD) and back pressure pump (BPP) was applied, allowing to control pressure and drilling fluid outflow besides drilling, during connections. The drilling conducted using reduced mud weight in «near balanced» conditions, which compared to conventional strategy sufficiently reduced formation overbalance and losses risk as well. Specialized MPD tests used to determine formation and fracturing pressure limit in uncertainty geological conditions, optimizing core sampling drilling and mud roll-over strategy.